# Reproducibility Barriers: A Checklist The most common reproducibility barrier is unrecorded work. A study looks solid in print, yet the exact data, code, protocol details, and parameter settings that produced the result were never saved in a form another person could use. The first thing to check is your own desk. Take your most recent result and ask whether a stranger could rerun it today using only the files, notebooks, and repositories you actually have. If the answer is no, you have found your first barrier. This article works through the barriers in order of how often they block real research, explains the mechanism behind each failure, and closes with prevention practices. The core is a reproducibility barriers checklist in table form, where each row pairs a symptom with its likely cause, a way to confirm the cause, and a fix. ## What Is a Reproducibility Barrier? ![Comparison table of reproducibility, replicability and robustness showing what each tests, what it needs and what a failure means.](/article-images/illustrations/reproducibility-barriers-checklist-three-targets.webp) A reproducibility barrier is any property of a study, a workflow, or the surrounding research culture that prevents other researchers from obtaining the same result from the same inputs, or from testing whether the result holds under new conditions. Research on the problem distinguishes three related concepts [1]. Analytical reproducibility means the same data and the same methods produce the same findings. Replicability means an effect appears again in a new dataset collected with the same or similar methods. Robustness to analytical variability means a finding survives when reasonable variations in method are applied. A barrier can attack any of the three. Barriers cluster into three families. Technical barriers include missing code, broken software environments, and unstandardized data formats. Cultural barriers include incentive systems that reward novelty over verification and a preference for compelling stories over reliable estimates. Structural barriers include the training, staffing, and funding assumptions that determine how much care any single study can receive [2]. Most failures involve at least one barrier from each family at the same time, which is why single-point fixes rarely work. ## The Reproducibility Barriers Checklist: Symptoms, Causes, and Fixes Use the table as a diagnostic instrument. Find the symptom that matches your situation, confirm the cause before acting, and then apply the fix. The sections after the table explain the mechanisms, ordered from the most common barrier to the rarest. | Symptom | Likely cause | How to confirm | Fix | |--|--|--|--| | A published result cannot be rerun from the paper alone | Key methodological parameters are unreported, such as [preprocessing](/blog/data-analysis/what-is-data-preprocessing) steps, reagent sources, or software settings | Audit the methods section against a field-specific reporting checklist | Complete a reporting checklist before submission and archive the full protocol with the data | | Code runs on one laptop but fails everywhere else | Undocumented software environment, unpinned package versions, absolute file paths | Run the project on a clean machine or fresh container and record every error | Pin versions, include an environment file, switch to relative paths, and containerize the pipeline | | Repeated runs of the same analysis give different numbers | Uncontrolled randomness, unset random seeds, nondeterministic parallel execution | Run the pipeline twice with logging on and compare the outputs | Set seeds, log hardware and library versions, and save each run's configuration | | The paper says data or code are available but nothing is findable | Materials were never deposited, links rotted, or a repository was abandoned | Check the paper, the supplements, and the stated URLs, then contact the corresponding author | Deposit code and data in a persistent repository with a DOI at submission time | | A dataset cannot be found, opened, or merged with others | Missing or nonstandard metadata and inconsistent variable definitions | Ask whether a stranger could find, open, and interpret the dataset unaided | Adopt community metadata standards and deposit in a structured repository | | The effect shrinks or vanishes in a new sample | Small sample size and low [statistical power](/blog/guides/statistical-power-what-it-is-and-why-it-matters-in-research) | Run a power calculation using the original effect size | Increase the sample, report the power analysis, and frame small-sample findings as preliminary | | Different defensible analysis choices lead to different conclusions | Analytical variability and researcher degrees of freedom | Vary preprocessing and modeling choices across reasonable options and compare outcomes | Preregister the primary analysis and report robustness across alternatives | | A computational model cannot be loaded, rerun, or extended | Missing annotation, nonstandard formats, undocumented parameters | Attempt to import the model into a community-standard tool | Annotate models in community-standard formats and share parameters and initial conditions | | A deep learning model performs well internally but fails elsewhere | Weak [generalizability](/blog/data-analysis/generalizability-in-research), undisclosed training details, unreleased weights | Check whether weights, training data descriptions, and evaluation code are public | Follow a model reporting checklist covering reproducibility, integrity, dependability, generalizability, and efficiency | | Nobody attempts or publishes replications | Incentive structures reward novelty while verification work goes unrewarded | Ask whether your journals, funders, and institution credit replication | Publish replications where they are welcomed, cite them, and push for explicit credit | Rows one through five are technical and account for most everyday failures. Rows six through ten involve statistics and culture, which take longer to fix but cause the deepest damage. ## Barrier 1: Undocumented Methods and Missing Materials ### Why reporting gaps happen Most method detail lives as tacit knowledge in one person's head. When that person graduates or changes labs, the lab loses the ability to recreate its own conditions. The scale of the problem is measurable. In event-related potential (ERP) research, where field reporting guidelines were updated in 2014, a review of 150 articles from five high-impact journals published between 2011 and 2017 found that authors reported an average of 63 percent of the recommended methodological parameters, such as preprocessing steps [3]. Publication of the guidelines had no effect on reporting behavior, which reveals the mechanism. Guidelines change practice only when editors and peer reviewers enforce them [3]. The fix is structure rather than goodwill. Structured protocols that capture reagent sources, lot numbers, equipment settings, and timing convert private knowledge into a shared artifact that survives personnel changes. **Safety note:** Protocols that involve biological reagents, cell lines, human-derived samples, or animal work carry biosafety and ethics obligations. A protocol written for public sharing must meet institutional biosafety standards, and any human-derived materials must be handled under the appropriate approvals before the protocol is released. ### When code and data are described but absent The same failure pattern appears wherever computation is involved. In machine learning research, many papers are not reproducible even in principle because the data and source code were never published and results are sensitive to training conditions. A survey of machine learning-driven research reached the same conclusion, listing unpublished data and code alongside sensitivity to training conditions as the leading causes. The problem is not confined to machine learning. When software engineering researchers attempted to reproduce two published duplicate-question detection methods, DupPredictor and Dupe, neither implementation was publicly available, the barriers to reproduction were high, and both reproductions lost recall as the underlying data grew over time [4]. Links rot, students move on, and the phrase "available on request" fails in practice. Depositing materials in a persistent repository with a DOI at submission time, rather than at revision time, is the reliable solution. ## Barrier 2: Data Without Metadata Metadata is data about data, the descriptions that let a stranger find a dataset, understand what each variable means, and judge whether the conditions of collection match their needs. Data sharing without usable metadata produces repositories full of files that nobody can reuse. The FAIR principles name the target state. A dataset should be findable, accessible, interoperable, and reusable. The gap between routine sharing and FAIR sharing is now well documented. After the National Institutes of Health implemented its updated Data Sharing Policy in 2023, researchers were found to share data routinely, yet the datasets were often not findable, accessible, interoperable, or reusable in practice. Three interdependent barriers drive the gap, a lack of incentives, little standardization in metadata collection and access, and uncoordinated efforts across repositories and communities. In omics research, where datasets are large and heavily dependent on experimental context, the barriers to metadata sharing are broader still. They include the lack of uniform standards, privacy and legal concerns, limitations built into study designs, limited incentives, inadequate infrastructure, and a shortage of trained personnel for metadata management and reuse. The proposed solutions mirror the barriers, promotion of standardization, education, engagement by journals and funding agencies, explicit rewards, and better infrastructure. **Safety note:** Privacy and legal constraints on human data are legitimate barriers, not obstacles to route around. De-identify human-derived datasets and confirm consent terms and institutional approvals before any deposit. ## Barrier 3: Small Samples and Low Statistical Power Statistical power is the probability that a study will detect an effect of a given size if that effect is real. Low power is a quiet barrier because a study can be published, peer reviewed, and cited while remaining nearly blind to the effects that matter most. The ERP literature provides concrete numbers. Across the same 150-article sample, the average sample size per group was 21 [3]. Estimated statistical power reached .72 to .98 for large effect sizes, fell to .35 to .73 for medium effects, and dropped to .10 to .18 for small effects [3]. In plain terms, these studies were powered mainly to detect large effects. A true small or medium effect would usually go undetected, and when a low-powered study does reach significance, the estimated effect tends to be inflated by chance. The mechanism explains why single small studies mislead. Noise dominates the estimate, so the published number reflects the sample as much as the phenomenon. The structural fix is to pool resources. Analyses of psychology's replicability, generalizability, and reproducibility challenges argue that they share a common cause, insufficient investment of intellectual and material resources in the typical single study, and that big-team science, where many researchers pool samples and effort, directly addresses it [2]. ## Barrier 4: Analytical Variability and Researcher Degrees of Freedom Most data analyses involve a long chain of defensible choices, filtering thresholds, exclusion rules, normalization schemes, model specifications. Each choice is reasonable on its own. The concern is the spread of outcomes across the reasonable choices. A finding that appears only under one pipeline is fragile, while a finding that survives many pipelines is robust [1]. Neuroimaging research, where preprocessing and modeling decisions are numerous, has made robustness to analytical variability an explicit third target alongside reproducibility and replicability [1]. Culture amplifies the technical problem. Commentators on cognitive neuroscience have argued that the field favors storytelling over reliable research and that the reproducibility crisis there is underappreciated [5]. A compelling narrative selects which analyses get reported and which get filed away, and selection across many small choices can manufacture a clean story from noisy data. The solutions are procedural. Preregister the primary analysis before the data are seen, report all measured outcomes, and run robustness checks across the defensible alternatives. Treating the analysis plan as a commitment device, rather than a suggestion, removes most of the degrees of freedom that cause trouble. ## Barrier 5: Fragile Environments and Unreusable Models Software is an experimental apparatus that degrades silently. Package versions change, dependencies conflict, and operating systems move on, so a pipeline that ran last year may not run today. Surveys of scientists who program find that many do not follow established best practices for reproducible scientific programming, and the named barriers are low self-efficacy and misaligned incentive structures rather than lack of interest [6]. The proposed remedies are better tooling, better education, and better incentives. Computational models face a parallel problem. In systems biology, models of complex dynamic systems accumulate quickly, but a model can only be reused and its virtual experiments reproduced if it is correctly annotated in community-supported, standardized formats [7]. An unannotated model is a black box, its parameters and initial conditions locked inside a file nobody else can interpret. Community-standard formats and thorough annotation turn a model from a one-off result into an instrument other labs can use [7]. The pattern extends across computational fields. In building performance simulation research, reproducibility is often overlooked because of technical complexity, insufficient documentation, and cultural barriers such as the lack of incentives for sharing code and data, and one response has been to propose recognizing reproducible code and data, given persistent DOIs, as peer-reviewed archival publications with practical workflows in MATLAB and Python [8]. The fixes stack. Use version control from the first day of a project, pin package versions in an environment file, wrap the pipeline in a container, set random seeds, and log every run. None of these steps requires new science, only the habit of treating the computational environment as part of the methods section. ## Barrier 6: Incentives, Culture, and Career Stage The rarest barrier to remove, and the hardest, is the reward system itself. Individual researchers rationally optimize for publications, so verification work loses to novelty work even when everyone agrees verification matters. Career stage sharpens the problem. Early career researchers form the predominant workforce in many disciplines, yet their contributions to solving the reproducibility crisis are hindered by the established academic system, and they are often an afterthought when solutions are designed [5]. Any reform that ignores the people who actually run the experiments will fail at the bench. Field surveys show how uneven the ground is. In geography, a probability-sampled survey of active researchers found a clear division in perceptions of reproducibility between subfields, varying familiarity with reproducible research practices, and a perceived lack of incentives to attempt or publish reproduction studies, alongside common foundations such as genuine interest in publishing transparent methods and in reproducing others' work for learning and validation [9]. A cross-disciplinary analysis of 122 published resources on reproducibility found that the same core issues recur regardless of subject area, which means solutions can be borrowed across fields once they are adapted [10]. Culture also produces a subtler failure, the appearance of openness without its substance. In computational social science, researchers have described open-washing, the practice of releasing minimal materials to claim credit for openness, and responded with a tier system that grades reproducibility by the level of external verifiability a study actually supports [11]. Genuine improvement means considering reproducibility proactively at the design stage rather than decorating a finished project [11]. The leverage points sit above individual labs. In translational biomedicine, poor data quality, weak reporting, and deficient reproducibility make the path from basic discovery to clinical use inefficient and costly, and improving it requires careful analysis and consistent enforcement at both the funding and peer review levels [12]. Proposals to give citable, DOI-bearing credit to shared code and data aim directly at the incentive problem. ## A Diagnostic Sequence for Any Suspicious Result ![Diagram of the diagnostic sequence, from choosing a result to logging each failure as a named barrier and applying the matching fix.](/article-images/illustrations/reproducibility-barriers-checklist-diagnostic-sequence.webp) Work through these steps in order. Each one either clears the result or names a specific barrier from the table above. 1. Choose one result you care about, ideally one of your own. 2. Gather everything a stranger would need, the data, the code, the protocol, and the parameter settings. 3. Rerun the analysis on a clean machine or a fresh container, recording every failure. 4. Compare the outputs to the published numbers and note every discrepancy. 5. Score the reporting completeness against a field-specific checklist. 6. Run a power calculation for the design using the observed effect size. 7. Change one defensible analysis choice, rerun, and compare conclusions. 8. Log each failure point as a named barrier, then apply the matching fix. A result that passes all eight steps is in strong shape for analytical reproducibility. The remaining questions, replicability and robustness, require new data and varied methods, which is where collaboration and replication efforts begin. ## Prevention: Building Reproducibility In From the Start Prevention costs less than diagnosis. The practices below are organized by project stage. **At planning.** Preregister the hypotheses and the primary analysis, or submit to a registered report format where review happens before data collection. Run a power analysis and size the sample for the effect you actually expect, not the effect you hope to publish. Write a [data management](/blog/guides/data-management-basics-principles-processes-and-best-practices) plan that names the repository, the metadata standard, and the access conditions in advance. **While doing the work.** Use version control from day one. Capture reagent sources, lot numbers, equipment settings, and timing in a structured protocol as you go, not from memory afterward. Set random seeds and log every computational run with its software versions. Annotate computational models in community-standard formats so they remain loadable and extensible [7]. **At sharing.** Deposit data, code, and protocols in a persistent repository with a DOI at submission time. Include an environment file or container so the pipeline runs elsewhere. Complete a reporting checklist before submission, and for deep learning segmentation models use a framework that explicitly assesses reproducibility, integrity, dependability, generalizability, and efficiency [13]. Write metadata to a community standard so the dataset is genuinely reusable rather than merely present. **At the level of the lab and field.** Consider reproducibility proactively in study design instead of retrofitting it [11]. Where a question is too large for one lab, consider pooled big-team approaches that raise the resources behind a single study [2]. Support enforcement at funding and review levels, and give explicit credit to replication and to shared materials [12]. ## Common Mistakes and Limitations - **Treating reproducibility as proof of truth.** A result can be perfectly reproducible and still be wrong. Reproducibility checks internal consistency, while validity requires replicability, robustness, and convergent evidence from independent approaches [1]. - **Confusing sharing with reusability.** Depositing a raw file dump without metadata creates the appearance of openness with little external verifiability, the pattern described as open-washing [11]. - **Assuming a checklist is the finish line.** Checklists are diagnostic tools. The fix is the behavior they prompt, the archived protocol, the pinned version, the power calculation, not the completed form itself. - **Copying a checklist across fields without adaptation.** A neuroimaging pipeline checklist will not ask about reagent lot numbers, and a wet-lab checklist will not ask about random seeds. Adapt the instrument to the workflow. - **Waiting until the revision stage.** Materials gathered after acceptance are usually incomplete. Deposit at submission, when the working versions still exist. - **Ignoring the human layer.** The strongest technical workflow fails if the people running it were never trained or never rewarded, which is why education and incentives appear as solutions in nearly every analysis of the problem. This checklist addresses process, and process is where most failures live. Individual studies still require field-specific judgment, mentor input, and ethics or biosafety review where applicable. ## Frequently Asked Questions ### What is a reproducibility barriers checklist? It is a structured list of the failure points that stop other researchers from rerunning or extending a study, paired with a way to confirm each cause and a matching fix. The table in this article is one example, organized by symptom. ### What are the most common causes of irreproducible research? Missing documentation and unshared code, data, and methods lead the list, followed by small samples with low statistical power, analytical variability, fragile software environments, and reward systems that favor novelty over verification. ### How do I fix reproducibility barriers in my own project? Start by rerunning your pipeline on a clean machine, then deposit code and data with a DOI, complete a field reporting checklist, preregister your primary analysis, and write metadata to a community standard. ### What is the difference between reproducibility and replicability? Reproducibility means the same data and the same methods produce the same result. Replicability means a new dataset, collected with the same or similar methods, shows the same effect. Robustness adds the test of varied methods. ### Why do small sample sizes create reproducibility problems? Small studies are powered mainly to detect large effects, so true small and medium effects often go undetected, and significant results from small samples tend to overstate the size of the effect. ### What exactly should I share to make my research reproducible? Share the raw data, the analysis code with an environment file, the full protocol with reagent sources and lot numbers, model files where relevant, and metadata written to a community standard. ### How can an early career researcher improve reproducibility without authority? Adopt low-cost practices inside your own project, document as you work, use checklists, and raise questions of enforcement and credit through journals, societies, and funders, where the real leverage sits. ### Are there ready-made reproducibility checklists I can adopt today? Yes. Fields publish their own, including reporting guidelines in electrophysiology, FAIR guidance for data, the RIDGE checklist for deep learning segmentation models, and general preregistration templates for study design. ## Sources 1. [Reproducibility in Neuroimaging Analysis: Challenges and Solutions.](https://pubmed.ncbi.nlm.nih.gov/36906444/) 2. [The Benefits, Barriers, and Risks of Big-Team Science](https://pubmed.ncbi.nlm.nih.gov/36190899/) 3. [Methodological reporting behavior, sample sizes, and statistical power in studies of event-related potentials: Barriers to reproducibility and replicability.](https://pubmed.ncbi.nlm.nih.gov/31322285/) 4. [Duplicate question detection in stack overflow: A reproducibility study](https://doi.org/10.1109/SANER.2018.8330262) 5. [Barriers and solutions for early career researchers in tackling the reproducibility crisis in cognitive neuroscience.](https://pubmed.ncbi.nlm.nih.gov/30670310/) 6. [Barriers to Reproducible Scientific Programming](https://doi.org/10.1109/VLHCC.2019.8818907) 7. [Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology](https://pubmed.ncbi.nlm.nih.gov/35671510/) 8. [The imperative for reproducibility in building performance simulation research](https://doi.org/10.1080/19401493.2024.2441385) 9. [Reproducible Research Practices and Barriers to Reproducible Research in Geography: Insights from a Survey](https://doi.org/10.1080/24694452.2023.2276115) 10. [Reproducibility literature analysis - a federal information professional perspective](https://doi.org/10.29173/iq967) 11. [Computational reproducibility in computational social science](https://doi.org/10.1140/epjds/s13688-024-00514-w) 12. [Reproducibility, Relevance and Reliability as Barriers to Efficient and Credible Biomedical Technology Translation.](https://pubmed.ncbi.nlm.nih.gov/35066104/) 13. [RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models](https://pubmed.ncbi.nlm.nih.gov/39557736/)